Benchmarking Spike Rate Inference in Population Calcium Imaging. Theis, L., Berens, P., Froudarakis, E., Reimer, J., Rom??n Ros??n, M., Baden, T., Euler, T., Tolias, A. S., & Bethge, M. Neuron, 90(3):471–482, 2016. ISBN: 1097-4199 (Electronic) 0896-6273 (Linking)doi abstract bibtex A fundamental challenge in calcium imaging has been to infer spike rates of neurons from the measured noisy fluorescence traces. We systematically evaluate different spike inference algorithms on a large benchmark dataset (\textgreater100,000 spikes) recorded from varying neural tissue (V1 and retina) using different calcium indicators (OGB-1 and GCaMP6). In addition, we introduce a new algorithm based on supervised learning in flexible probabilistic models and find that it performs better than other published techniques. Importantly, it outperforms other algorithms even when applied to entirely new datasets for which no simultaneously recorded data is available. Future data acquired in new experimental conditions can be used to further improve the spike prediction accuracy and generalization performance of the model. Finally, we show that comparing algorithms on artificial data is not informative about performance on real data, suggesting that benchmarking different methods with real-world datasets may greatly facilitate future algorithmic developments in neuroscience.
@article{Theis2016,
title = {Benchmarking {Spike} {Rate} {Inference} in {Population} {Calcium} {Imaging}},
volume = {90},
issn = {10974199},
doi = {10.1016/j.neuron.2016.04.014},
abstract = {A fundamental challenge in calcium imaging has been to infer spike rates of neurons from the measured noisy fluorescence traces. We systematically evaluate different spike inference algorithms on a large benchmark dataset ({\textgreater}100,000 spikes) recorded from varying neural tissue (V1 and retina) using different calcium indicators (OGB-1 and GCaMP6). In addition, we introduce a new algorithm based on supervised learning in flexible probabilistic models and find that it performs better than other published techniques. Importantly, it outperforms other algorithms even when applied to entirely new datasets for which no simultaneously recorded data is available. Future data acquired in new experimental conditions can be used to further improve the spike prediction accuracy and generalization performance of the model. Finally, we show that comparing algorithms on artificial data is not informative about performance on real data, suggesting that benchmarking different methods with real-world datasets may greatly facilitate future algorithmic developments in neuroscience.},
number = {3},
journal = {Neuron},
author = {Theis, Lucas and Berens, Philipp and Froudarakis, Emmanouil and Reimer, Jacob and Rom??n Ros??n, Miroslav and Baden, Tom and Euler, Thomas and Tolias, Andreas S. and Bethge, Matthias},
year = {2016},
pmid = {27151639},
note = {ISBN: 1097-4199 (Electronic) 0896-6273 (Linking)},
keywords = {\#nosource},
pages = {471--482},
}
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